Difference-guided video analysis

ABSTRACT

A method can include obtaining, from a video having a first resolution, a set of frames having a second resolution. The first resolution can be higher than the second resolution. The set of frames can include a first frame and a second frame. The method can include generating a difference feature map. The method can include obtaining a third frame having the first resolution. The method can include detecting, based on the difference feature map, a first location of a first object in the third frame. The method can include cropping, from the third frame, a first cropped area. The first cropped area can be smaller than a third frame area. The method can include generating, based on a feature map and the difference feature map, a spatial attention layer. The method can include detecting, by the spatial attention layer, the first object in the first cropped area.

BACKGROUND

The present disclosure relates to video analysis, and more specifically,to high-definition video analysis.

Video analysis can include detecting objects and their locations withinframes of a video, such as a digital video recording. Video analysis canfurther include operations such as object classification and motionrecognition of detected objects. In some instances, at least oneconvolutional neural network (CNN) can be employed to analyze a video toperform a video analysis operation. The effectiveness of video analysiscan be increased with increased resolution of a video that is analyzed.

SUMMARY

According to embodiments of the present disclosure, a method can includeobtaining, from a video having a first resolution, a set of frameshaving a second resolution. The first resolution can be higher than thesecond resolution. The set of frames can include a first frame and asecond frame adjacent to the first frame. The method can includegenerating, based on the first frame and the second frame, a differencefeature map. The method can include obtaining, from the video, a thirdframe having the first resolution. The third frame can have a thirdframe area. The method can include detecting, based on the differencefeature map, a first location of a first object in the third frame. Themethod can include cropping, from the third frame, a first cropped areacorresponding to the first object. The first cropped area can be smallerthan the third frame area. The method can include generating a firstfeature map of the first cropped area. The method can includegenerating, based on the first feature map and the difference featuremap, a spatial attention layer. The method can include detecting, by thespatial attention layer, the first object in the first cropped area.

A system and a computer program product corresponding to the abovemethod are also included herein.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 depicts an example computing environment having adifference-guided video analysis system, in accordance with embodimentsof the present disclosure.

FIG. 2 depicts example video frames analyzed in accordance withembodiments of the present disclosure.

FIG. 3 depicts an example convolutional neural network that includes aspatial attention layer, in accordance with embodiments of the presentdisclosure.

FIG. 4 depicts a flowchart of an example method for performingdifference-guided video analysis operations, in accordance withembodiments of the present disclosure.

FIG. 5 depicts the representative major components of a computer systemthat can be used in accordance with embodiments of the presentdisclosure.

FIG. 6 depicts a cloud computing environment according to embodiments ofthe present disclosure.

FIG. 7 depicts abstraction model layers according to embodiments of thepresent disclosure.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to video analysis; moreparticular aspects relate to difference-guided video analysis. While thepresent disclosure is not necessarily limited to such applications,various aspects of the disclosure may be appreciated through adiscussion of various examples using this context.

Video analysis can include detecting objects and their locations withinframes of a video, such as a digital video recording. Video analysis canfurther include operations such as object classification and motionrecognition of detected objects. In some instances, at least oneconvolutional neural network (CNN) can be employed to analyze a video toperform a video analysis operation. The effectiveness of video analysiscan be increased with increased resolution of a video that is analyzed.For example, in some instances, video objects having a size of 32 orfewer pixels can be detected with higher accuracy in a video having aresolution of (7,680×4,320) pixels (hereinafter, “8K resolution”) thanin a video having a resolution of (720×576) pixels.

However, the increased resolution of such a video can also presentchallenges. For example, processing a video having a high resolution(e.g., a resolution of approximately (1,280×720) pixels to approximately8K resolution or higher) can increase processing times and/or a cost oftools, such as memory and/or processors, employed to process the video.In some instances, processing such high-resolution videos can consume abandwidth sufficient to burden systems and/or networks.

To address these and other challenges, embodiments of the presentdisclosure include a difference-guided video analysis system (“DGS”).According to embodiments of the present disclosure, the DGS can improvean efficiency of object detection in higher-resolution frames of a videothrough the use of a difference feature map generated fromlower-resolution frames of the video. More specifically, in someembodiments, the DGS can obtain video data, such as a digital videohaving an 8K resolution. From the digital video, the DGS can extractadjacent frames and convert the adjacent frames to a lower resolution(e.g., (720×480) pixels). The DGS can generate a difference feature mapfrom a difference between the lower-resolution adjacent frames. Based onthe difference feature map, the DGS can determine a location of anobject in higher-resolution frames of the video. Based on the location,the DGS can crop an area that corresponds to the object from thehigher-resolution frames. The DGS can further detect the object withinthe cropped area of higher-resolution, employing spatial attention thatis based, in part, on the difference feature map. Thus, in effect,embodiments of the present disclosure can use a difference betweenlower-resolution video frames to “guide” the detection of objects inhigher-resolution video frames.

Accordingly, by using lower-resolution frames to detect objects in ahigher-resolution video, embodiments of the present disclosure canreduce time and/or resources that would be employed to process thehigher-resolution video for object detection. Embodiments of the presentdisclosure can improve the field of video analysis by improvingprocessing efficiency while maintaining or improving an accuracy ofobject detection in high-resolution (e.g., 8K resolution) videos. Insome embodiments, the DGS can improve an accuracy of one or more videoanalysis operations by employing a spatial attention layer.

Turning to the figures, FIG. 1 illustrates a computing environment 100that includes one or more of each of a DGS 105, a computer device 120, aserver 130, and/or a network 135. In some embodiments, at least one DGS105, computer device 120, and/or server 130 can exchange data with atleast one other through the at least one network 135. One or more ofeach of the DGS 105, computer device 120, server 130, and/or network 135can include a computer system, such as the computer system 501 discussedwith respect to FIG. 5 .

In some embodiments, the DGS 105 can be included in software installedon a computer system of at least one of the computer device 120 and/orserver 130. For example, in some embodiments, the DGS 105 can beincluded as a plug-in software component of software installed on acomputer device 120. The DGS 105 can include program instructionsimplemented by a processor, such as a processor of a computer device120, to perform one or more operations discussed with respect to FIGS.2-4 .

In some embodiments, the DGS 105 can include one or more modules, suchas a data manager 110 and/or image analyzer 115. In some embodiments,the data manager 110 and the image analyzer 115 can be integrated into asingle module. In some embodiments, the data manager 110 can obtain,interpret, analyze, store, and/or initiate storage of data, such asvideo data 125. In some embodiments, the image analyzer 115 can employimage processing, editing, and/or analysis technology to analyze data,such as video data 125. In some embodiments, image analyzer 115 caninclude a CNN. In some embodiments, the data manager 110 and/or imageanalyzer 115 can include program instructions implemented by aprocessor, such as a processor of a computer device 120, to perform oneor more operations discussed with respect to FIGS. 2-4 . For example, insome embodiments, the data manager 110 can include program instructionsto perform operations 405 and 410, FIG. 4 . In some embodiments, theimage analyzer 115 can include program instructions to performoperations 415-435, FIG. 4 .

In some embodiments, the one or more computer devices 120 can includeone or more desktop computers, laptops, tablets, and the like. In someembodiments, the one or more computer devices 120 can include video data125. In some embodiments, the video data 125 can include informationsuch as videos (e.g., digital video files) and/or video frames/images.In some embodiments, video data 125 can include informationcorresponding to video analyses, such as RGB values, predeterminedthresholds, and the like. In some embodiments, video data 125 can beincluded on one or more servers 130. In some embodiments, the one ormore servers 130 can include one or more web servers.

In some embodiments, the network 135 can be a wide area network (WAN), alocal area network (LAN), the internet, or an intranet. In someembodiments, the network 135 can be substantially similar to, or thesame as, cloud computing environment 50 discussed with respect to FIG. 6.

FIG. 2 illustrates an example video 205 from which a DGS (e.g., DGS 105,FIG. 1 ) can generate a spatial attention layer 265, according toembodiments of the present disclosure. The video 205 can have an 8Kresolution. From the video 205, the DGS can extract a first frame 210and a second frame 215. The first frame 210 and the second frame 215 canbe adjacent frames of the video 205 and can each have a resolution of(720×480) pixels. The first frame 210 and a second frame 215 can becomposed of a respective set of pixels that make up the images visiblein the first frame 210 and the second frame 215. Each pixel can have alocation (e.g., X and Y coordinates in the corresponding frame).Additionally, a color of each pixel can correspond to a set of Red GreenBlue (“RGB”) values. For example, the color white can correspond to RGBvalues (255, 255, 255), and the color black can correspond to RGB values(0, 0, 0).

The DGS can generate a difference feature map image 240, which canrepresent a difference between the first frame 210 and the second frame215. For example, generating the difference feature map image 240 caninclude subtracting the second frame 215 from the first frame 210. Thesubtracting operation can correspond to subtracting the RGB values foreach pixel location in the second frame 215 from the RGB values for eachcorresponding pixel location in the first frame 210. In the differencefeature map image 240, the regions that appear black can indicate pixellocations where the subtracting operation results in RGB values that donot exceed a threshold, such as RGB values less than (100, 100, 100).Such RGB values can indicate that there is no significant differencebetween the first frame 210 and the second frame 215 in those regions.In contrast, in the difference feature map image 240, the regions thatappear white can indicate pixel locations where the subtractingoperation results in RGB values exceed a threshold, such as RGB valuesgreater than (100, 100, 100). Such RGB values can indicate motion of anobject between the first frame 210 and the second frame 215.

From the difference feature map image 240 and/or the RGB valuescorresponding to the difference feature map image 240, the DGS candetect an approximate location of a first object 225 and a second object235 that exhibit movement between the first frame 210 and the secondframe 215. The DGS can generate a first bounding box 220 that encloses afirst bounding box area. The first bounding box area can include pixellocations corresponding to the first object 225. The DGS canadditionally generate a second bounding box 230 that encloses a secondbounding box area. The second bounding box area can include pixellocations corresponding to the second object 235.

From the video 205, the DGS can extract a third frame 245 having an 8Kresolution. Based on the first bounding box 220, the DGS can generate athird bounding box 250 for the third frame 245. The third bounding box250 can enclose a third bounding box area that includes pixel locationscorresponding to a third object 270. The third object 270 can correspondto the first object 225.

Based on the third bounding box 250, the DGS can extract a cropped area255 from the third frame 245. The cropped area 255 can include amagnified image of the third object. The DGS can generate a spatialattention layer 265 for a CNN based on the cropped area 255 and objectdata 260 corresponding to the first object 225 of the difference featuremap image 240 (see, e.g., formula (1), below). The DGS can employ thespatial attention layer 265 to classify and/or accurately locate thethird object 270 in subsequent 8K-resolution frames of the 8K-resolutionvideo 205. Accordingly, the DGS can classify and/or accurately locatethe third object 270 by processing the cropped area 255 as opposed toprocessing the entire frame 245. In this way, the DGS can reduce timeand/or resources for detecting (e.g., classifying and localizing) anobject in a high-resolution (e.g., 8K) video.

FIG. 3 illustrates an example CNN 300 having a spatial attention layer330, in accordance with embodiments of the present disclosure. In someembodiments, CNN 300 can be employed to detect objects in cropped areas305, generated in accordance with embodiments of the present disclosure.In some embodiments, cropped areas 305 can include one or more portionsof one or more frames (e.g., cropped area 255, FIG. 2 ). The CNN 300 caninclude a set of convolutional layers 310 configured to extract featuresof the cropped areas 305. In some embodiments, the set of convolutionallayers 310 can output a feature map 315 corresponding to features of acropped area 305. In some embodiments, the CNN 300 can include a headlayer 330 for performing a specific task, such as classification and/orrecognition. In some embodiments, a spatial attention layer 320generated in accordance with operations described with respect to FIGS.2 and 4 can be included between the set of convolutional layers 310 andthe head layer 330. In some embodiments, the spatial attention layer 320can output a refined feature 325 having spatial attention. For example,the refined feature 325 can be weighted to indicate an importance of aregion of a cropped area, such as a region corresponding to a locationof a detected object in a difference feature map. Thus, in someembodiments, the special attention layer 320 can regularize an output ofa CNN.

In some embodiments, the spatial attention layer 330 can include amatrix according to the formula:λΣ∥ω_(ij)x_(ij)−D_(ij)∥  (1), where:

λ is a preselected scale factor that can be selected by an entity, suchas a programmer of a DGS; ω_(ij)x_(ij) is a Sigmoid function applied toa 1 channel feature map corresponding to a cropped area (e.g., croppedarea 255, FIG. 2 ); and D_(ij) corresponds to values of a differencefeature map (e.g., RGB values corresponding to object data 260).

FIG. 4 illustrates a flowchart of an example method 400 for performingdifference-guided video analysis operations, in accordance withembodiments of the present disclosure. The method 400 can be performedby a DGS, such as the DGS 105 discussed with respect to FIG. 1 .

In operation 405, the DGS can obtain video data, such as a video filethat includes a digital video having a resolution (e.g., an 8Kresolution). The video data can include a set of frames, or images, eachhaving a resolution, such as an 8K resolution. In some embodiments, theDGS can obtain such video data from a device, such as a computer device(e.g., computer device 120, FIG. 1 ), a server (e.g., server 130 FIG. 1), and/or an image capture device, such as a camera.

In operation 410, the DGS can obtain a set of adjacent frames of avideo. In some embodiments, the set of adjacent frames can include twoframes arranged in sequential order in a video. For example, in someembodiments, if a video has a frame rate of 20 frames per second, theset of adjacent frames can include the first and second frames, theninth and tenth frames, or the nineteenth and twentieth frames of thevideo. The set of adjacent frames can have a lower resolution than aresolution of a source video (i.e., a video from which the set ofadjacent frames is obtained). For example, in some embodiments, the DGScan obtain a source video having a (3,840×2,160) pixel resolution(hereinafter “4K resolution”) in operation 405. Continuing with thisexample, in operation 410, the DGS can employ video editing tools toextract adjacent frames having a lower resolution, such as a (720×480)pixel resolution, from the source video.

In operation 415, the DGS can generate a difference feature map. In thisdisclosure, a difference feature map can refer to a representation of adifference between features of two frames (e.g., two adjacent frames) ofa video. In some embodiments, a difference feature map can include amatrix having a set of values corresponding to a set of pixel locationsof a frame or image. For example, in some embodiments, such a matrix caninclude sets of RGB values corresponding to pixel locations of an image.Continuing with this example, for a pixel location (x=0, y=0) (e.g., abottom, left pixel of an image), the matrix can store the RGB values (0,0, 0), which can represent the color black. Continuing with thisexample, for a pixel location (x=10, y=50), the matrix can store the RGBvalues (255, 255, 255), which can represent the color white. In thisexample, pixel locations having RGB values that represent the colorwhite can indicate pixel locations where there is a difference between afirst frame and a second frame of a video (see, e.g., difference featuremap image 240, discussed with respect to FIG. 2 ). In some embodiments,a difference feature map can include a difference feature map image(e.g., difference feature map image 240, FIG. 2 ).

In some embodiments, generating a difference feature map can include theDGS calculating a difference between RGB values corresponding to a firstframe and RGB values corresponding to a second frame of a video. In someembodiments, operation 415 can include the DGS performing a principalcomponent analysis of such calculated differences and/or clusteringoutputs corresponding to the calculated differences. In someembodiments, operation 415 can include the DGS selecting thresholds,such as a set of threshold RGB values that can indicate motion of anobject in a difference feature map. In some embodiments, the DGS canimplement machine learning processes to select such thresholds. In someembodiments, the thresholds can be selected by an entity, such as aprogrammer of DGS and/or a user of the DGS.

In operation 420, the DGS can detect a location of one or more objectsin the difference feature map generated in operation 415. For example,in some embodiments, operation 420 can include the DGS analyzing pixellocations of the difference feature map having RGB values that exceed athreshold. Such pixel locations can correspond to motion of an objectbetween adjacent frames of a video. Continuing with this example, theDGS can select an area of the difference feature map that includes thepixel locations having RGB values that exceed the threshold. Continuingwith this example, the DGS can generate a bounding box that enclosessuch an area, which can be referred to as a bounding box area. Thebounding box area can indicate an approximate location of an object inthe difference feature map.

In operation 425, based on the bounding box area selected in operation420, the DGS can obtain a cropped area from a third frame of a sourcevideo. The cropped area can include an object in the third frame thatcorresponds to an object detected in the difference feature map inoperation 420. The third frame can have a resolution that is higher thana resolution of the adjacent frames obtained in operation 410. Forexample, in some embodiments, operation 425 can include the DGSobtaining a cropped area of an 8K-resolution frame. In some embodiments,operation 425 can include the DGS translating a bounding box areaselected in operation 420 to a corresponding location in the thirdframe. For example, in some embodiments, the DGS can determine arelationship (e.g., a linear relationship) between pixel locations of adifference feature map having 720×480 pixels and corresponding pixellocations of a third frame having an 8K-resolution. Based on such arelationship, the DGS can identify a set of pixel locations of the8K-resolution frame that correspond to the pixel locations of thebounding box area of the difference feature map. In this example, theidentified corresponding set of pixel locations of the 8K-resolutionframe can be referred to as a translated bounding box area. In someembodiments, the translated bounding box area can be identical orsubstantially similar to the cropped area. The cropped area can besmaller than an area of the third frame, as the DGS can extract thecropped area from the third frame. In some embodiments, the DGS canmagnify an image of an object included in the cropped area. Suchmagnifying can improve object detection in subsequent frames obtainedfrom a source video (e.g. object detection by CNN 300, FIG. 3 ).

In operation 430, the DGS can generate a spatial attention matrixaccording to formula (1) discussed with respect to FIG. 3 . In someembodiments, the spatial attention matrix can be incorporated into aspatial attention layer of a CNN. The spatial attention matrix canweight pixel locations of frames that correspond to pixel locationswhere one or more objects are detected in a difference feature map.Accordingly, the spatial attention matrix can facilitate efficient,accurate detection of objects in frames.

In operation 435, the DGS can employ the spatial attention matrixgenerated in operation 430 to detect (e.g., locate and/or classify) anobject in a cropped area of a video frame.

FIG. 5 depicts the representative major components of an exemplaryComputer System 501 that can be used in accordance with embodiments ofthe present disclosure. The particular components depicted are presentedfor the purpose of example only and are not necessarily the only suchvariations. The Computer System 501 can comprise a Processor 510, Memory520, an Input/Output Interface (also referred to herein as I/O or I/OInterface) 530, and a Main Bus 540. The Main Bus 540 can providecommunication pathways for the other components of the Computer System501. In some embodiments, the Main Bus 540 can connect to othercomponents such as a specialized digital signal processor (notdepicted).

The Processor 510 of the Computer System 501 can be comprised of one ormore CPUs 512. The Processor 510 can additionally be comprised of one ormore memory buffers or caches (not depicted) that provide temporarystorage of instructions and data for the CPU 512. The CPU 512 canperform instructions on input provided from the caches or from theMemory 520 and output the result to caches or the Memory 520. The CPU512 can be comprised of one or more circuits configured to perform oneor methods consistent with embodiments of the present disclosure. Insome embodiments, the Computer System 501 can contain multipleProcessors 510 typical of a relatively large system. In otherembodiments, however, the Computer System 501 can be a single processorwith a singular CPU 512.

The Memory 520 of the Computer System 501 can be comprised of a MemoryController 522 and one or more memory modules for temporarily orpermanently storing data (not depicted). In some embodiments, the Memory520 can comprise a random-access semiconductor memory, storage device,or storage medium (either volatile or non-volatile) for storing data andprograms. The Memory Controller 522 can communicate with the Processor510, facilitating storage and retrieval of information in the memorymodules. The Memory Controller 522 can communicate with the I/OInterface 530, facilitating storage and retrieval of input or output inthe memory modules. In some embodiments, the memory modules can be dualin-line memory modules.

The I/O Interface 530 can comprise an I/O Bus 550, a Terminal Interface552, a Storage Interface 554, an I/O Device Interface 556, and a NetworkInterface 558. The I/O Interface 530 can connect the Main Bus 540 to theI/O Bus 550. The I/O Interface 530 can direct instructions and data fromthe Processor 510 and Memory 520 to the various interfaces of the I/OBus 550. The I/O Interface 530 can also direct instructions and datafrom the various interfaces of the I/O Bus 550 to the Processor 510 andMemory 520. The various interfaces can comprise the Terminal Interface552, the Storage Interface 554, the I/O Device Interface 556, and theNetwork Interface 558. In some embodiments, the various interfaces cancomprise a subset of the aforementioned interfaces (e.g., an embeddedcomputer system in an industrial application may not include theTerminal Interface 552 and the Storage Interface 554).

Logic modules throughout the Computer System 501—including but notlimited to the Memory 520, the Processor 510, and the I/O Interface530—can communicate failures and changes to one or more components to ahypervisor or operating system (not depicted). The hypervisor or theoperating system can allocate the various resources available in theComputer System 501 and track the location of data in Memory 520 and ofprocesses assigned to various CPUs 512. In embodiments that combine orrearrange elements, aspects of the logic modules' capabilities can becombined or redistributed. These variations would be apparent to oneskilled in the art.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6 , illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N can communicate. Nodes 10 cancommunicate with one another. They can be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7 , a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 6 ) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 7 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities can be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 can provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources can comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment can be utilized. Examples of workloads andfunctions which can be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and difference-guided video analysis logic96.

As discussed in more detail herein, it is contemplated that some or allof the operations of some of the embodiments of methods described hereincan be performed in alternative orders or may not be performed at all;furthermore, multiple operations can occur at the same time or as aninternal part of a larger process.

The present invention can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions can also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:obtaining, from a video having a first resolution, a set of frameshaving a second resolution, the first resolution being higher than thesecond resolution, wherein the set of frames comprises a first frame anda second frame adjacent to the first frame; generating, based on thefirst frame and the second frame, a difference feature map; obtaining,from the video, a third frame having the first resolution, the thirdframe having a third frame area; detecting, based on the differencefeature map, a first location of a first object in the third frame;cropping, from the third frame, a first cropped area corresponding tothe first object, the first cropped area being smaller than the thirdframe area; generating a first feature map of the first cropped area;generating, based on the first feature map and the difference featuremap, a spatial attention layer of a convolutional neural network; anddetecting, by the spatial attention layer, the first object in the firstcropped area.
 2. The computer-implemented method of claim 1, wherein thegenerating the difference feature map comprises calculating a differencebetween first values corresponding to first pixels of the first frameand second values corresponding to second pixels of the second frame. 3.The computer-implemented method of claim 2, wherein the first values andthe second values comprise Red Green Blue (RGB) values.
 4. Thecomputer-implemented method of claim 1, further comprising generating,by the spatial attention layer, an output; and inputting the output intoa head of the convolutional neural network.
 5. The computer-implementedmethod of claim 1, wherein the detecting the first location comprises:identifying, in the difference feature map, a set of pixel locationshaving respective values that exceed a threshold; selecting a boundingbox area that includes the set of pixel locations; and translating thebounding box area to the third frame, resulting in a translated boundingbox area, wherein the translated bounding box area comprises the firstcropped area.
 6. The computer-implemented method of claim 1, wherein thedetecting the first object comprises regularizing, by the spatialattention layer, an output of the convolutional neural network.
 7. Thecomputer-implemented method of claim 1, wherein the spatial attentionlayer comprises a calculation of a difference between a weighted outputof the convolutional neural network and values of the difference featuremap.
 8. The computer-implemented method of claim 1, wherein the firstresolution is (7680×4320) pixels and the second resolution is (680×480)pixels.
 9. A system comprising: one or more processors; and one or morecomputer-readable storage media storing program instructions which, whenexecuted by the one or more processors, are configured to cause the oneor more processors to perform a method comprising: obtaining, from avideo having a first resolution, a set of frames having a secondresolution, the first resolution being higher than the secondresolution, wherein the set of frames comprises a first frame and asecond frame adjacent to the first frame; generating, based on the firstframe and the second frame, a difference feature map; obtaining, fromthe video, a third frame having the first resolution, the third framehaving a third frame area; detecting, based on the difference featuremap, a first location of a first object in the third frame; cropping,from the third frame, a first cropped area corresponding to the firstobject, the first cropped area being smaller than the third frame area;generating a first feature map of the first cropped area; generating,based on the first feature map and the difference feature map, a spatialattention layer of a convolutional neural network; and detecting, by thespatial attention layer, the first object in the first cropped area. 10.The system of claim 9, wherein the generating the difference feature mapcomprises calculating a difference between first values corresponding tofirst pixels of the first frame and second values corresponding tosecond pixels of the second frame.
 11. The system of claim 10, whereinthe first values and the second values comprise Red Green Blue (RGB)values.
 12. The system of claim 9, the method further comprisinggenerating, by the spatial attention layer, an output; and inputting theoutput into a head of the convolutional neural network.
 13. The systemof claim 9, wherein the detecting the first location comprises:identifying, in the difference feature map, a set of pixel locationshaving respective values that exceed a threshold; selecting a boundingbox area that includes the set of pixel locations; and translating thebounding box area to the third frame, resulting in a translated boundingbox area, wherein the translated bounding box area comprises the firstcropped area.
 14. The system of claim 9, wherein the detecting the firstobject comprises regularizing, by the spatial attention layer, an outputof the convolutional neural network.
 15. The system of claim 9, whereinthe spatial attention layer comprises a calculation of a differencebetween a weighted output of the convolutional neural network and valuesof the difference feature map.
 16. A computer program product comprisingone or more computer readable storage media, and program instructionscollectively stored on the one or more computer readable storage media,the program instructions executable by one or more processors to causethe one or more processors to perform a method comprising: obtaining,from a video having a first resolution, a set of frames having a secondresolution, the first resolution being higher than the secondresolution, wherein the set of frames comprises a first frame and asecond frame adjacent to the first frame; generating, based on the firstframe and the second frame, a difference feature map; obtaining, fromthe video, a third frame having the first resolution, the third framehaving a third frame area; detecting, based on the difference featuremap, a first location of a first object in the third frame; cropping,from the third frame, a first cropped area corresponding to the firstobject, the first cropped area being smaller than the third frame area;generating a first feature map of the first cropped area; generating,based on the first feature map and the difference feature map, a spatialattention layer of a convolutional neural network; and detecting, by thespatial attention layer, the first object in the first cropped area. 17.The computer program product of claim 16, wherein the generating thedifference feature map comprises calculating a difference between firstvalues corresponding to first pixels of the first frame and secondvalues corresponding to second pixels of the second frame.
 18. Thecomputer program product of claim 17, wherein the first values and thesecond values comprise Red Green Blue (RGB) values.
 19. The computerprogram product of claim 16, the method further comprising generating,by the spatial attention layer, an output; and inputting the output intoa head of the convolutional neural network.
 20. The computer programproduct of claim 16, wherein the detecting the first location comprises:identifying, in the difference feature map, a set of pixel locationshaving respective values that exceed a threshold; selecting a boundingbox area that includes the set of pixel locations; and translating thebounding box area to the third frame, resulting in a translated boundingbox area, wherein the translated bounding box area comprises the firstcropped area.